Exemplos de uso de An eigenvalue em Inglês e suas traduções para o Português
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Colloquial
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Medicine
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Ecclesiastic
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Computer
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Official/political
Own subspace associated with an eigenvalue.
Thus λ is an eigenvalue of W-1AW with generalized eigenvector W-kv.
For example, if the matrix is orthogonal, then 1 or-1 is an eigenvalue.
This component presented an eigenvalue of 5.38, explaining 26.9% of the total variance.
To determine the number of factors,we used the criterion of an eigenvalue> 1.
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This solution produced a factor with an eigenvalue of 3.58 and explained 59.7% of the total variance.
So, with the transversality idea, we can de ne when an eigenvalue is stable.
Factors with an eigenvalue> 1 on principal component analysis were included in varimax rotation.
The criterion used to extract factors was to retain all factors with an eigenvalue higher than 1.
With an eigenvalue of 2.88, this component contributed to 14.4% of total variance; its Cronbach's alpha was 0.73.
This happens more generally if the algebraic andgeometric multiplicities of an eigenvalue do not coincide.
Factor 2 worries had an eigenvalue of 1.18, being responsible for 13% of the variance and involved items 1, 4, and 6.
This may happen when solve returns a not-so-obviously real expression for an eigenvalue which is known to be real.
The number of factors with an eigenvalue superior to one and Cattell's Scree test determined the number of factors for extraction.
The second empirical factor, feelings about politics with 7 items,has presented an Alpha of .72, with an eigenvalue of 3.17.
Thus, the method converges slowly if there is an eigenvalue close in magnitude to the dominant eigenvalue. .
Part of the result states that a non-zero complex number in the spectrum of a compact operator is an eigenvalue.
Of the factors derived from those seven variables,three presented an eigenvalue> 1 and were therefore included in the analyses.
An eigenvalue of the adjacency matrix of a graph is said to be main when it has an eigenvector that is not orthogonal to the vector whose coordinates are equal to1.
Thus, based on 21 items, two factors were extracted with an eigenvalue superior to 1.5 and an explained variance of 46.02.
An eigenvalue>1 was used as a criterion for retention factors, and the load value of 0.4 to consider that a specific item is being represented in a factor.
The principal contrasts analysis displayed the presence of an eigenvalue component 22.4, explaining 55.5% of the variance of the items.
The multiplicity of 0 as an eigenvalue is the nullity of P, while the multiplicity of 1 is the rank of P. Another example is a matrix A that satisfies A2 α2I for some scalar α.
Factor 1 positive attitudes had an eigenvalue of 2.86, being responsible for 32% of the variance and involved items 5, 7, 10, and 11.
The factor IV was composed of the following three items: pain, fatigue andshortness of breath, with an eigenvalue of 1.06, explaining 56.65% of the total variance.
With the model data,the module performs an eigenvalue analysis so that the result of the instability of the affected structural component is shown graphically.
The factor III was comprised of three items, namely: drowsiness,dry mouth, and numbness/tingling, with an eigenvalue of 1.21, explaining 44% of the total variance.
If an eigenvalue algorithm does not produce eigenvectors,a common practice is to use an inverse iteration based algorithm with μ set to a close approximation to the eigenvalue. .
More generally, if W is any invertible matrix, andλ is an eigenvalue of A with generalized eigenvector v, then(W-1AW- λI)k W-kv 0.